Partners HealthCare showcases 3 innovative analytics use cases

From decision support optimization to EHR performance improvement, the Boston health system puts its data to work in new and interesting ways.

Brigham and Women's Hospital, part of Partners HealthCare, has recently focused on data science to improve clinical decision support and EHR functionality. (Photo: Partners)

Most hospitals use analytics to improve quality and drive cost efficiencies, but some leverage data in more innovative ways than others. In Boston this week, leaders from Partners HealthCare showcase the creative use cases for number-crunching.

At the HIMSS Big Data and Healthcare Analytics Forum last week, Partners staffers walked through three unique analytics use cases they developed, focused on nursing, decision support and EHR performance optimization.

Encouraging data-driven nursing

One of the key challenges at Partners eCare – the health system division focused on leveraging its Epic system to the fullest extent possible – was promoting appreciation of data for all clinicians, including nurses.

Nurses are well-suited to understanding the value of analytics-driven decision-making, she explained. They're motivated by compassion and empathy, sure, but are also detail-oriented and guided by best practices.

The key to getting them to buy in to data, she said, is to "make it accessible, make it relevant and make sure they know they own it."

The challenge in driving data as a critical component in nursing culture is embedding data into the everyday life of a nurse, Swenson explained. Ingrained data literacy depends on making the data meaningful, timely and easily available to the RNs who depend on it.

At Partners, a Nurse Manager Quality Safety Dashboard incorporated those principles – as well as conscientious design elements to reduce clutter and highlight actionable metrics – was able to encourage nurses to incorporate analytic more effectively in their care delivery workflows.

It helped engender trust and appreciation of the stories told by EHR data, boosted efficiency in managing clinical operations and improved real-time detection of deficiencies in certain areas.

Most of all, it "helped them be much more efficient," said Swenson of her nurses. "Now they really trusted the data."

Along the way, Partners learned some valuable lessons about what she called the "building blocks of data-driven culture." Good data is centralized and organized, embedded in workflow from the start, easy to analyze and interpret, tactical and relevant.

Smart analytics initiatives enables nurses to focus on core group of metrics, said Swenson, with layered access that supports nurse manager decision making

Optimizing EHR performance

When Partners decided to implement Epic several years back, a long and expensive rollout, it was a big deal since the health system had been famous for its own pioneering homegrown system.

"We had a long history of internal development before Epic," said cardiologist Amy Miller, MD Director of Medical Services at Partners eCare and Brigham and Women's.

That track record of self-developed expertise also led to "strong opinions and lofty goals" once Epic was deployed, she added. The EHR is known for its customization, of course, but Partners "took it to the extreme," she said. "We have a long history of over-engineering."

Oftentimes, Epic staff would try to warn them off certain system adjustments, but Partners often did them anyway, confident of their ability to make them work, but sometimes they wouldn't work, pushing the EHR to the extent that it was "significant performance load on the system to pull information from all the related encounters," said Miller.

Over time, "with the increased load of more related encounters, the load time for the report was increasing," she added.

But when the EHR was sub-performing for clinicians it wasn't always necessarily obvious, she explained. The fact that they weren't complaining vocally about it did not mean there wasn't a problem.

"Waiting for users to tell us about performance issues is waiting too long," said Miller. "We need to be able to monitor the system itself.

"We wanted to use data from the system to better predict is there a problem, before it impacts end-users in a deleterious fashion," she added.

Partners also wanted to gather data about which add-on EHR functionalities were actually useful – and used – rather than simply desired for the sake of it ("I know I've never clicked on it. But I might!") to help the system run more smoothly.

"Users have strong perceptions of wants and needs that fly in the face of the data," said Miller, noting that, even with data showing limited use, subject matter experts saved a number of data elements that objectively should have been eliminated."

The analytics data gathered for this purpose helped the EHR run more efficiently and effectively, she said, enabling it to restructure form content based on usage pattern, change scripting accordingly, use the short form of common and very frequently documented elements, etc.

Many of the challenges and drags on the system were clearer once seen in black and white – it just took data and analytics to help show the divergence between what the clinical end users thought they needed and what the system could deliver efficiently.

Making CDS work better

Adam Wright, senior scientist at Partners' Brigham and Women's Hospital has focused his data science expertise recently on clinical decision support. Not so much on CDS itself however, but rather on the task monitoring the systems and tracking how well they work.

After all, as we've seen from study after study about the perils of alert fatigue, "too much alerting or alerting of the wrong kind can lead to provider frustration," said Wright.

And that's not even counting when CDS misfires or offers inaccurate information, he added, noting that ONC has found many "safety issues and unintended consequences" from sub-optimally configured decision support.

Partners has had its own issues, said Write, pointing to an alert that indicating that a two-year-old child getting a lead screening should also be asked about his smoking status.

The ways Wright and his team were able to sift through various alert anomalies were complex, and relied on Poisson change-point analyses, Bayesian models and other detailed analytical concepts.

But data science – along with a comment log where Partners trouble-shooters could scan the (often colorfully phrased) complaints and feedback from clinicians – enabled the health system to do extensive work tailoring and streamlining its CDS systems to work more effectively.